翻译

ORE-AGE: AN INTELLIGENT TUTORING SYSTEM MODEL
FOR MINING METHOD SELECTION
CENK GURAY
Atilim University, Industrial Eng. Dept. Ankara, Turkey
Mining method selection is a critical decision for an economic, safe and productive mining work. Each ore body is unique with its own properties and engineering judgment has a great effect on the decisions. In this study an intelligent assisting and tutoring system for preliminary underground mining method selection is developed.
采矿方法选择对于经济的、安全的以及富有成效的采矿工作至关重要。

每一个矿体有自己独特的特点并且工程判断对于决定有着重大的影响。

在这一研究中，发展了一种智能的辅助体系，这一体系用于初步的地下采矿方法选择。

This system is called Ore-Age, whose goal is making the preliminary mining method selection as efficiently as possible and to make the users perform this selection as efficiently as possible too while giving them a remarkable education on mining method selection. 这一体系叫做矿产年代，目的是初级采矿方法筛选尽可能有效并且使得应用者也能够非常有效的运用这一体系，在给这些使用者一些培训之后。

Due to its semi-autonomous character, Ore-Age determines to direct its execution strategy based on the expertise levels of the users. 由于它的半自动性，矿产年代决定着执行的策略，并适用于专业人员。

Ore-Age acts as an assisting tool for the experienced engineers during the selection process and continuously looks for chances to direct the experiences of such expert engineers to his own database by the help of its neuro-fuzzy learning algorithm. 矿产年代在筛选过程中作为有经验的工程人员的一种辅助工具并且不断地为寻
找机会来引导这些有经验的工程人员建立自己的数据库，这需要在神经模糊运算的帮助下进行。

The reason of Ore-Age to take this learning procedure is to imitate and behave like these experts during his future selections.矿产年代的采取这种学习过程的原因就是在未来的筛选中模仿并且按专家的形式来开展。

Besides this ability, Ore-Age tries to act as a tutoring system as efficiently as possible when he faces inexperienced engineers. 除了这一能力之外，矿产年代也尝试着在面对没有经验的工程人员时，可以尽可能高效的作为一种培训系统。

The regular strategy of teaching process depends on an iterative algorithm checking the decisive concepts one by one to find the point of misconception leading to a wrong selection. 教授进程的通常的策略
取决于一个重复的计算，来一个接一个的检查决定性的该概念来找出错误概念导致错误选择的重点。

Furthermore as an alternative strategy, by his error modeling property, Ore-Age can alter his strategy and concentrate the users directly to the possible points of misconception, without using the previous “time demanding” algorithm. 另外，作为一种可替换的策略，通过错误模型性能，矿产年代能够替换它的策略并且使使用者直接集中到错误概念的那一点，而不使用先前的“时间需要”计算。

This system aiming the model the cognitive behavior of the student is an indication of the reactive characteristic of the system that can alter the strategies based on his own logical decision to achieve a more efficient tutoring procedure. 这一体系目的在于学生的认知
行为的模型，是一种对能够基于他自己的逻辑决定来获得更有效的辅导过程的体系的反应特征的解释。

The system that is being developed in this study can be introduced as the first example of dynamic, intelligent assisting and tutoring systems in the mining profession.这一研究中的这一体系可以在采矿专业中引进作为第一个动态的，智能辅助的以及培训体系的案例。

1. Introduction: Through Intelligent Tutoring Systems To Mining Business
Intelligent Tutoring Systems (ITS) seem to be one of the most pioneering advances of the last
century in the area of education. Instead of Static Systems which choose to perform the educational processes within a previously defined structure; ITS propose more flexible approaches utilizing strategies differing according to the qualifications of the student. In other words, ITS provide individualized instruction by being able to be adapted to the knowledge learning abilities and needs of individual student1. By their ability of modeling the student’s current state of knowledge and individualizing their instruction based on that model; ITS are eligible to provide non-judgmental and tailored feedback continuously this shall lead to an obvious efficient learning state for the students2. At the present time, ITS are continuously improving at the edge of a new horizon opening way to Affective Tutoring Systems (ATS), which detect nonverbal behavior and use this information to individualize interactions with the student. The advancement in this area will make it possible to model not only the knowledge state but also the cognitive and emotional states of the student concluding into the achievement of more effective teaching strategies by the Tutoring System.
The area of mining engineering with its versatile and complicated activities give rise to new and fruitful areas for the application of intelligent systems mainly concentrating on the needs of the area about tutoring and assisting. Especially, mining method selection work; both by requiring a formidable route for its process and by occupying a central position for all the mining activities; constitute a really effective case for such interdisciplinary applications.
The production in a mine is carried out by the use of systematic approaches called mining methods. Mining methods are said to be the core of the mining engineering by most of the miners, since the selection of a specific mining method for production carries great importance for the economics, safety and the productivity of the mining work. This critical decision about mining method selection should be given by the mining engineer and the determined method should provide healthy working conditions for the workers, a protective working process for the environment, a profitable job for the company and a productive mine for the welfare of the country. However, the procedure of making this selection can be very confusing and difficult because of many reasons, but particularly; each ore body is unique with its own properties.
Therefore, for each new ore body, the mining engineer may need to put forward new ideas or judgments. There are two solid requirements of mining engineering business about this process regarding the computer science. One is the need for an assistance medium to help the professional engineers to judge in such a versatile and hard decision. The other is the need for a tutoring medium for presenting and teaching the mining engineering students the mining method selection work, based on the intuitive selection processes of the expert mining engineers. Computer aided tutoring and assistance seem to be the best alternatives to design such media after our short review about their capabilities. The aim in this study is to design and implement an intelligent tutoring (ITS) and assisting system that can help the users both during the selection of a mining method and during the teaching procedure required beforehand for an efficient selection.
2. The Theory of Intelligent Tutoring Systems
After such a brief introduction to detect the borderlines of the study, some theoretical knowledge about the intelligent systems what we call ITS, will make the reader more engaged in the concept。

Intelligent tutoring systems should have the capability of interacting with the learner (i.e. student) as well as modeling the learner. The modeling process is crucial to detect the student’s beliefs and misconceptions directly from the student’s a nswers to diagnostic tests or through
tracing the learner’s actions. Furthermore, it is essential to provide adaptive feedback2. ITS identify and interpret a student’s problem (diagnosis) and then using a suitable strategy, it communicates with the student to overcome or correct the problem (remediation).
It is generally agreed among researchers that an intelligent tutoring system should possess the following three models which act as different but interacting modules through its execution processes 3.
• Do main model, which contains the knowledge about the domain to be taught.
• Student model, which represents the emerging knowledge and the skills of the student.
• Tutoring model that designs and regulates the instructional interactions with the student.
A user interface is also indispensable within such a framework to present the possibility of interacting with the users. The system in this study possesses these three modules and a user interface to perform the duties of an ITS.
An ITS uses the information achieved from its three knowledge-based models to guide the student’s interaction with the system4. During the interaction, the intelligent tutoring system has to recognize and correct any student errors that might occur5. Experience and recent research increasingly call for adaptive remediation of student errors, and remediation is increasingly viewed as a central part of the overall tutoring process6. Therefore, the teaching process can be divided into four separate consecutive functions4,7.
• Planning of a series of teaching actions.
• Monitoring of the execution of these actions within the student, i.e. the student behavior is being compared against the expected outcome in order to detect any possible errors.
• Diagnosis procedure about any of the detecte d errors in order to determine the cause of the specific error.
• Remediation of the error.
Of course it should be stated clearly that most intelligent systems have been developed based on the certain assumption that students’ cogitation process can be mod eled, traced and corrected within a problem solving context using computers8. The success of the systems volunteer for performing such a mission is hidden in the consistency, efficiency and the realism of the designed cognitive model aiming to imitate the learning process of the student. But, how this complicated process can open a new horizon in a similarly complex branch of the mining engineering business, namely mining method selection ?
3. A Real Challenge in the Area: Inserting the ITS into the Problem of the Mining
Method Selection
In the literature, some decision support systems are proposed to assist, especially the inexperienced engineers, in selecting mining methods; however these pioneering studies also possess some points need to be recreated wit h today’s vision. Bandophadyay and Venkatasubramanian developed one of the first studies on the application of expert systems in the area of mining method selection9. This system was developed to help the mining engineers in selecting mining methods for coal deposits mineable by underground methods.
For the initial system, several geotechnical factors such as rock strength, groundwater and floor condition that influence the selection of a mining method were chosen. Values for the certainty factor in each production rule were obtained from the expert opinion. Camm and
Smith10 developed another study on the application of expert systems in the area of mining method selection. Within this study, a mining and milling method selection expert was described
using a knowledge base that is composed of alternative methods, experience, intuition, deposit types (that are studied beforehand based on geologic data and expert knowledge), mine plans and engineering studies. Another expert system was developed by Gershon, Bandopadhyay and Panchanadam11 based on Nicholas’ methodology12, 16 of mining method selection also creating the basement for this work too which will be discussed in details soon. By using multi-attribute utility theory which provides a convenient way of handling preference and attitudes in
decision-making, the system’s choices are based on the users’ objectives with priorities such as safety. In the study performed by Tatiya13, a mining method is selected between three stopping methods namely sublevel, down the hole and cut and fill based on a detailed economic analysis. Basu14 then developed a similar expert system by improving practically and technically the system of Gershon, Bandopadhyay and Panchanadam11.
All of the decision support systems described in the previous paragraph are static systems lacking the capability of giving interactive decisions to increase the selection efficiency. In addition, they are not capable of embedding the intuition and the judgment simulating experienced engineers in the selection process. Besides, not only for mining method selection procedure but also for the rest of the subjects in mining discipline, there exists no solid and comprehensive system designed for educational purposes. A successful educational tool based on a dynamic rich database can make the students and junior engineers learn the mining method selection scheme better and easier. The possible diagnostic and remedial character of such a tool can overcome misconceptions and misunderstandings during the learning procedure.
In this study an intelligent tutoring and assisting system is developed for initial mining method selection. The system is composed of two sub-systems: one is for tutoring while the other is for assisting. An interface agent called Ore-Age provides the necessary connections between subsystems and the users controlling the execution of the system. Ore-Age can select the
sub-system to be operated based on the expertise level of the users, so the system can be evaluated as a semi-autonomous one. The expertise levels of the users depend solely on the previous interaction experience of the system with these users. This characteristic of the system nicely coincides with the definition of the autonomy for such intelligent agents: “An agent is autonomous to the extent that its behavior is determined by its own experience” 15. Two sub-systems work based on virtual experts that are developed to give decisions about the mining method selection work. These virtual experts have the ability to learn and to put new knowledge into their databases.
A neuro-fuzzy hybrid system is used for the learning process. Therefore, the system can insert the intuition of the expert engineers systematically to the selection criteria by the help of its learning ability. The agent Ore-Age acting as the core of the system, can take the role of an interactive tutor during the tutoring session. He finds out the possible misconceptions of the student by asking questions, giving knowledge and reacting according to the answers coming from the students. He also has the ability of modeling the students, or the students’ knowledge of mining method selection by modeling the common errors that they make. The error modeling procedure embedded in the system aims to execute the remediation procedure more effectively and in a faster way, by determining directly the possible point of the error based on the error model and directing the user to this point without spending extra time on finding the point of the error. In this way, Ore-Age can alter his teaching strategy to a more efficient way while giving a nice example of an efficient application of the stated theoretical procedure of the ITS to this specific area. The goal of the agent Ore-Age, is to make an initial mining method selection as efficient as possible
and to orient the users to perform this efficient selection too while giving them a useful tutorial supplying a remarkable education on mining method selection.
The next section of the paper gives a methodological background about mining method selection, while section five gives a system overview. The procedures leading to the implementation are given in the sixth section of the paper. In order to make a demonstration for the system, some case studies are executed by the use of the developed system. The details of execution and the results of these case studies are given in the seventh section of the paper. Two engineers, one of them being a junior while the second one being a senior are selected as the users in this study. Their performances will be followed through out the seventh section of the paper mostly questioning the performance of the system as a tutor. In this way, at the end, the reader will have been presented with a small tutorial that will show the possible outcomes of different procedures of the software. The last subsection of section seven is dedicated to performance evaluation, which gives the reader an idea about the performance of the implemented software in tutoring and learning as an intelligent tutoring system. Section eight gives the results and the conclusions about the study.
4. Methodology of mining method selection
Among several studies of designing a methodology for mining method selection, Nicholas’ work12 has an important characteristic of collecting and systemizing all criteria which have been put forward by the other authors and the new ideas he proposed in a single methodology. Because of this reason and because of the algorithmic structure of his system, this study concentrates more on his work and uses certain parts of this system as the background knowledge base. During this study, Nicholas’ methodology is revised and some necessary additions and updates are also performed.
According to Nicholas12, two important factors that have a great impact on the selection of
a mining method are as follows.
1. Geometry and grade (amount of valuable mineral found in unit weight of deposit, eg.
gr/ton) distribution of the deposit.
2. Rock mass strength for the ore zone, the hangingwall (the rock above the orebody), and the footwall (the rock below the orebody).
The geometry of the deposit (orebody) is defined in terms of the general shape (with subtopics of massive, platy-tabular and irregular), ore thickness (with subtopics of narrow, intermediate, thick, very thick), plunge (with subtopics of flat, intermediate and steep) and depth below surface. On the other hand, grade distribution of the orebody is defined being as uniform, gradational or erratic. According to Nicholas, the rock mechanics characteristics of the orebody that can be considered as critical during the mining method selection can be identified by the topics of rock substance strength(having weak, moderate and strong categories), fracture spacing (having very close,close, wide, very wide categories) and fracture strength (having weak, moderate and strong characteristics). As the most important step in the selection process of Nicholas’ methodology, after the characteristics of the orebody are learned, the applicability of each of the mining methods for this orebody is evaluated by assigning certain points (weights) to the mining methods. These weighting policies can be changed based on personal experience. The points assigned to the methods increase or decrease with direct proportion to the suitability of the orebody characteristics for that specific method. For this purpose, each of the ore characteristics starting from ore geometry to boundary shape are investigated and evaluated for each of the
mining methods. If the investigated ore characteristic makes the mining method more applicable for the given orebody a higher point is given to the method or vice versa. Therefore, if the characteristic is preferred for the mining method, 3-4 points are assigned. If the mining method can be used with the given characteristic without any problem 1-2 points are assigned. If it is unlikely that the mining method would be applied by the given characteristic, but the method is not completely ruled out, 0 point is given. If the mining method completely rules out the method –49 points are given. Therefore, following this logic, the suitability of the orebody to each method is evaluated and the feasible ones can be chosen after a numerical ranking process based on the total points collected by each method.
The methods determined to be most applicable are then considered in terms of mining rate, labor availability, environmental concerns and other site-specific considerations in order to determine whether these parameters will eliminate any method from further consideration. It should be stated clearly that; Nicholas’ methodology and thus our system only performs the initial selection step for a mining method; further studies are carried out by experienced mining engineers based on the above parameters and their engineering judgment to finalize the decision process.
As indicated by Nicholas12, the purpose of the numerical method selection system is not to choose the final mining method. In fact, there is no single appropriate mining method for a deposit; there are usually two or more feasible methods. Each method entails some inherent problems. Consequently, the optimum method is that method with the least problems. So Nicholas’ methodology is intended to indicate those methods that will be most effective given
geometry/grade distribution and rock mechanics characteristics, and which are worthy for further studies in the next stage of the selection process. The mining methods that will be used throughout the study are as follows:
1. Undercut and fill,
2. longwall mining,
3. sublevel stoping,
4. block caving,
5. sublevel caving,
6. room and pillar,
7. cut and fill,
8. top slicing,
9. square set,
10. shrinkage stoping,
11. longitudinal sublevel stoping,
12. longitudinal sublevel caving,
13. longitudinal block caving.
Throughout the study, the methods are sometimes denoted with the specific number coinciding with their place in the above sequence.
The mining method selection procedure embedded in this system has the following flow of operations:
1. Required characteristics and the related numerical or verbal inputs to define these characteristics for the evaluation of the orebody against different mining methods are determined.
2. User indicates and enters the values of these characteristics for the given orebodyinto the
system as either numerical or verbal inputs. Certain points are assigned to numerically evaluate these characteristics by the system, which are called asweights11,15. Weights reflect the suitability of the specific characteristics for the execution of any of the mining methods.
3. For each mining method, the weights taken for each criterion are added up for making the evaluation of all the characteristics with respect to all of the mining methods possible. Then, all the methods are ranked according to the amount of points they have collected. The methods with higher ranks are selected to be suitable for the given orebody.
4. Three methods determined to be most applicable are then recommended to the user by assigning a sequence based on the priorities proportional to the points they have acquired. These three methods are naturally the three methods possessing the highest three ranks after the numerical evaluation and ranking procedure.
5. System Overview
The hybrid system is composed of three main elements as demonstrated in Figure 1:
• Users,
• Virtual experts giving decisions about the suitability of the mining methods to thecharacteristics of the given orebodies,
• Agent Ore-Age acting as an interface between these two elements.
In the system, users both enter the necessary inputs regarding the ore characteristics and receive the system responses about the selections. Having the advantage of an interactive system; the users may either acquire knowledge on mining method selection from the system or the system can acquire some knowledge from the users granting that they have an expert level of knowledge on mining method selection. This system works based on the evaluations of 13 different expert systems (virtual experts) corresponding to 13 different mining methods. These virtual experts possess the premium background knowledge and the evaluation capability about the mining methods embedded in this system. They numerically analyze the suitability of the mining methods with respect to the considered ore body evaluating each of the given ore characteristics based on the methodology described previously and send the results to Ore-Age.
Ore-Age is an interface agent between the users and the virtual experts. The goal of the agent Ore-Age, is to make the initial mining method selection as efficiently as possible and to orient the users to perform such an efficient selection too following a remarkable education on mining method selection. He receives the inputs from the users and sends them to the virtual experts; consecutively he receives the results coming from the experts and gives the final decisions about the selection process. Ore-Age also judges the expertise levels of the users and he either canalizes them to the tutoring mode to be trained or in the learning mode to acquire knowledge from them, according to their expertise levels. In other words, the decisions given by Ore-Age about the expertise levels of the users; by the help of his user-evaluation module; control the mode to be executed next. This flexible strategy-decisive ability is a direct indication of the semiautomated characteristic of the system. In order to fulfill the goals of the system, Ore- Age should have the ability to communicate with the virtual experts to make a selection, learning capacity to extend his information and tutoring capacity to educate the users16.
So briefly, the agent Ore-Age carries out three courses of action to realize his goal:
a) Method selection based on the ideas of the 13 virtual experts.
b) User evaluation to decide on the execution of either the tutoring or the learning
algorithm.
c) Execution of the tutoring or the learning algorithm.
As the first step in the execution of the system, Ore-Age evaluates the expertise level of the user based on his/her performance on a previously determined set of case studies, by the execution of the evaluation module. The execution of the system then continues with the acquirement of the necessary inputs about the ore characteristics from the users; in order to evaluate the presented orebodies to be produced against the potential mining methods. Ore-Age handles these data and transmits them to the virtual experts for evaluation. 13 virtual experts make their evaluations for the methods separately and remind Ore-Age about their numerical results reflecting the suitability of the ore-body with the methods they are dealing with. Ore-Age, then chooses the most suitable three methods, which are in fact the methods possessing the first three ranks after a numerical ranking process. These choices can also be called first, second and third degree of choices. If the user is classified as an expert in the evaluation module, the learning mode is executed by Ore-Age. First, the three alternatives chosen by Ore-Age are given to the user, and his/her choice is asked. If the user has a different choice, this choice is learned by the system to be an alternative choice for the similar cases to be faced in the future executions. These new potential choices are learned with degrees of reliability changing proportionally by the expertise levels of the users that are determined by Ore-Age. The higher the level of expertise of the user is, the higher the reliability of the information achieved from him/her. If it is decided in the evaluation module that the user is a new learner, the tutoring mode is executed, to train him/her on the area of mining method selection. The quality and the efficiency of the education will be further improved by introducing the error database, used for modeling the errors of the users. The details about these procedures are given in the next section, which will be followed by a system demonstration.
8. Conclusions
After the implementation of the described algorithms and procedures designing 13 virtual experts and one interface agent, a kind of reliable software for assisting and tutoring the initial mining method selection work is achieved. This application of artificial intelligence to the mining engineering discipline has two major contributions to the literature.
The first of the two major contributions of this study to the literature is related to the learning procedure embedded in the system. The neuro-fuzzy algorithm executed is supplying a base for the system and thus the users to learn and use the intuitive approach and therefore the judgment capability of the expert engineers towards the mining method selection work. The dynamic approach of this system aiming to shape its database according to the expert views and its intelligent ability to imitate the experts’ logic of problem solving, places this study in a specific state among the similar applications in the mining engineering discipline.
As stated at the beginning of the paper, an extensive part of the recent research on Artificial Intelligence promote Intelligent Tutoring Systems as new and efficient opportunities for supporting the area of education. Thus, the system’s interactive tutoring ability, which is activated for inexperienced users can be seen as the second contribution of this study to the literature. Up to now, not only for mining method selection procedure but also for the rest of the subjects in mining discipline, there exists no solid and comprehensive system designed for educational purposes. The tutoring part of the system can activate like a typical and efficient example of intelligent tutoring systems. Therefore the missing knowledge or the misconceptions of the inexperienced users can be completed or corrected in the most efficient and fastest way by the semiautomatic, diagnostic, remedial and pro-active characteristic of the system. The system or the interface agent Ore-Age,。